We propose to work cooperatively with other investigators funded by this U24 mechanism to evaluate proteomic technologies that will enable the early detection of several tumor types through the application of blood-based tests. Our group, consisting of scientists at LBNL, UCSF, The Buck Institute, MD Anderson Hospital, and the University of British Columbia, has the broad expertise that this project requires. We will focus on breast cancer. Initially, we will examine both global strategies and targeted mass spectrometry (MS)-based approaches to develop optimal workflows for the identification of protein signatures of human breast cancer cells in murine plasma. The global strategies will utilize multiple workflows that emphasize quantitative comparisons. The targeted approaches will focus on cancer-specific proteins that result from aberrant RNA splicing. Candidate biomarkers will be validated using reverse phase protein arrays. The requisite antibodies will be generated by Epitomics. The next phase of the project will employ human clinical samples. Specifically, we will apply an analogous, optimized approach for analyzing plasma samples that will be prospectively collected from breast cancer patients (n = 200). Control samples will be obtained from healthy women with benign breast disease and from women with rheumatoid arthritis to account for the contribution of proteins associated with inflammatory processes. Our candidate approach targets both the spliceome, which will be profiled using breast cancer biopsies from the plasma donors, and posttranslational modifications (e.g., glycosylation, phosphorylation, proteolysis and oxidative damage). We also include a plan for establishing a systematic way to standardize proteomic protocols and data analysis among the groups that exploits curability to analyze mass spectra generated on numerous platforms and the robust statistical methods we will employ to mine these large data sets. Several other elements of the RFA are also addressed. For example, we summarize the analysis capacity of our instruments and our strategy for sharing project-generated resources including biological specimens, protocols, data, software tools, and intellectual resources. In the end, we envision that our group, in conjunction with the other CPTAC teams and the NCI, will develop methods, tools and reference samples for the research community that will make the promise of MS-based cancer biomarker discovery a reality.